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Peptide-Protein Interface Classification Using Convolutional Neural Networks

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Advances in Bioinformatics and Computational Biology (BSB 2023)

Abstract

Peptides are short chains of amino acid residues linked through peptide bonds, whose potential to act as protein inhibitors has contributed to the advancement of rational drug design. Indeed, understanding the interactions between proteins and peptides is potentially helpful for several biotechnological applications. However, it is not a trivial task since peptides can adopt different conformations when interacting with proteins. In this paper, we develop a classification model for protein-peptide interfaces using a convolutional neural network and distance maps. To evaluate our proposal, we performed two case studies classifying protein-peptide interfaces based on peptide sequences and receptor classes. Additionally, we compared the distance map approach with a graph-based structural signatures approach. We aim to find out if a convolutional neural network could classify peptides just from the patterns of distances in these maps. In conclusion, graph-based methods were slightly superior in almost all comparisons performed. However, distance map-based signature methods achieved better results for some classes, such as classifying hormones, membranes, and viral proteins. These results shed light on the potential use of distance maps for classifying protein-peptide interfaces. Nevertheless, more experiments may be needed to explore this use.

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Notes

  1. 1.

    https://github.com/LBS-UFMG/cnn-distance-maps.

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Acknowledgements

The authors thank the funding agencies: CAPES, CNPq, and FAPEMIG.

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Correspondence to Lucas Moraes dos Santos .

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Santos, L.M.d., Mariano, D., Luiza Bastos, L., Cioletti, A.G., Minardi, R.C.d.M. (2023). Peptide-Protein Interface Classification Using Convolutional Neural Networks. In: Reis, M.S., de Melo-Minardi, R.C. (eds) Advances in Bioinformatics and Computational Biology. BSB 2023. Lecture Notes in Computer Science(), vol 13954. Springer, Cham. https://doi.org/10.1007/978-3-031-42715-2_11

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  • DOI: https://doi.org/10.1007/978-3-031-42715-2_11

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